{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                                                 \n",
    "import matplotlib.pyplot as plt                                      \n",
    "import seaborn as sns                                            \n",
    "import numpy as np                                                   \n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pip\n",
      "  Downloading https://files.pythonhosted.org/packages/54/2e/df11ea7e23e7e761d484ed3740285a34e38548cf2bad2bed3dd5768ec8b9/pip-20.1-py2.py3-none-any.whl (1.5MB)\n",
      "Installing collected packages: pip\n",
      "  Found existing installation: pip 19.2.3\n",
      "    Uninstalling pip-19.2.3:\n",
      "      Successfully uninstalled pip-19.2.3\n",
      "Successfully installed pip-20.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pip --upgrade --force-reinstall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('Train.csv')\n",
    "df_test = pd.read_csv('Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233, 7)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(543, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                    0\n",
       "Location_Score          0\n",
       "Internal_Audit_Score    0\n",
       "External_Audit_Score    0\n",
       "Fin_Score               0\n",
       "Loss_score              0\n",
       "Past_Results            0\n",
       "IsUnderRisk             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                    0\n",
       "Location_Score          0\n",
       "Internal_Audit_Score    0\n",
       "External_Audit_Score    0\n",
       "Fin_Score               0\n",
       "Loss_score              0\n",
       "Past_Results            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "      <td>15.666</td>\n",
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       "      <td>15</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>79.243</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>541</td>\n",
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       "      <td>8</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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       "      <td>542</td>\n",
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       "      <td>12</td>\n",
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       "<p>543 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "0       2           8.032                    14                     8   \n",
       "1      31          77.730                     8                     3   \n",
       "2      40          59.203                     3                    12   \n",
       "3      12          73.080                     4                     5   \n",
       "4       4          15.666                    13                    15   \n",
       "..    ...             ...                   ...                   ...   \n",
       "538    16          74.017                     7                     4   \n",
       "539     2          70.460                     7                     5   \n",
       "540     1          79.243                     7                     5   \n",
       "541    40          69.140                     7                     8   \n",
       "542    13          23.332                    14                    12   \n",
       "\n",
       "     Fin_Score  Loss_score  Past_Results  IsUnderRisk  \n",
       "0            3           6             0            1  \n",
       "1            3           8             1            0  \n",
       "2           11           3             0            1  \n",
       "3            7           6             0            0  \n",
       "4            6           7             2            1  \n",
       "..         ...         ...           ...          ...  \n",
       "538          5           7             1            0  \n",
       "539          6           4             0            0  \n",
       "540          3           8             1            0  \n",
       "541          4           5             1            1  \n",
       "542         10           3             2            1  \n",
       "\n",
       "[543 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "      <th>IsUnderRisk</th>\n",
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       "      <td>City</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.049994</td>\n",
       "      <td>-0.051779</td>\n",
       "      <td>0.033183</td>\n",
       "      <td>0.004762</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>Location_Score</td>\n",
       "      <td>0.012551</td>\n",
       "      <td>1.000000</td>\n",
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       "    </tr>\n",
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       "      <td>Internal_Audit_Score</td>\n",
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       "      <td>-0.358629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453839</td>\n",
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       "      <td>0.007685</td>\n",
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       "      <td>0.586097</td>\n",
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       "    <tr>\n",
       "      <td>External_Audit_Score</td>\n",
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       "      <td>0.141163</td>\n",
       "      <td>0.435619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fin_Score</td>\n",
       "      <td>-0.051779</td>\n",
       "      <td>-0.264536</td>\n",
       "      <td>0.365133</td>\n",
       "      <td>0.433374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.078573</td>\n",
       "      <td>0.138111</td>\n",
       "      <td>0.362463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Loss_score</td>\n",
       "      <td>0.033183</td>\n",
       "      <td>-0.042139</td>\n",
       "      <td>0.007685</td>\n",
       "      <td>-0.038786</td>\n",
       "      <td>-0.078573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081858</td>\n",
       "      <td>0.044888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Past_Results</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>-0.074364</td>\n",
       "      <td>0.112093</td>\n",
       "      <td>0.141163</td>\n",
       "      <td>0.138111</td>\n",
       "      <td>0.081858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IsUnderRisk</td>\n",
       "      <td>-0.087463</td>\n",
       "      <td>-0.423956</td>\n",
       "      <td>0.586097</td>\n",
       "      <td>0.435619</td>\n",
       "      <td>0.362463</td>\n",
       "      <td>0.044888</td>\n",
       "      <td>0.102120</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          City  Location_Score  Internal_Audit_Score  \\\n",
       "City                  1.000000        0.012551             -0.024306   \n",
       "Location_Score        0.012551        1.000000             -0.358629   \n",
       "Internal_Audit_Score -0.024306       -0.358629              1.000000   \n",
       "External_Audit_Score -0.049994       -0.205775              0.453839   \n",
       "Fin_Score            -0.051779       -0.264536              0.365133   \n",
       "Loss_score            0.033183       -0.042139              0.007685   \n",
       "Past_Results          0.004762       -0.074364              0.112093   \n",
       "IsUnderRisk          -0.087463       -0.423956              0.586097   \n",
       "\n",
       "                      External_Audit_Score  Fin_Score  Loss_score  \\\n",
       "City                             -0.049994  -0.051779    0.033183   \n",
       "Location_Score                   -0.205775  -0.264536   -0.042139   \n",
       "Internal_Audit_Score              0.453839   0.365133    0.007685   \n",
       "External_Audit_Score              1.000000   0.433374   -0.038786   \n",
       "Fin_Score                         0.433374   1.000000   -0.078573   \n",
       "Loss_score                       -0.038786  -0.078573    1.000000   \n",
       "Past_Results                      0.141163   0.138111    0.081858   \n",
       "IsUnderRisk                       0.435619   0.362463    0.044888   \n",
       "\n",
       "                      Past_Results  IsUnderRisk  \n",
       "City                      0.004762    -0.087463  \n",
       "Location_Score           -0.074364    -0.423956  \n",
       "Internal_Audit_Score      0.112093     0.586097  \n",
       "External_Audit_Score      0.141163     0.435619  \n",
       "Fin_Score                 0.138111     0.362463  \n",
       "Loss_score                0.081858     0.044888  \n",
       "Past_Results              1.000000     0.102120  \n",
       "IsUnderRisk               0.102120     1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e871a804c8>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_train.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e86ec74e48>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_train.drop('IsUnderRisk',axis=1),diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using H2O to find the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    " import h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "; Java HotSpot(TM) 64-Bit Server VM (build 25.251-b08, mixed mode)\n",
      "  Starting server from C:\\Users\\niran\\Anaconda3\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n",
      "  Ice root: C:\\Users\\niran\\AppData\\Local\\Temp\\tmpb8mz0n8d\n",
      "  JVM stdout: C:\\Users\\niran\\AppData\\Local\\Temp\\tmpb8mz0n8d\\h2o_niran_started_from_python.out\n",
      "  JVM stderr: C:\\Users\\niran\\AppData\\Local\\Temp\\tmpb8mz0n8d\\h2o_niran_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>02 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Asia/Kolkata</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.30.0.1</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 14 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_niran_u90zos</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>3.505 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O_API_Extensions:</td>\n",
       "<td>Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.7.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------------\n",
       "H2O_cluster_uptime:         02 secs\n",
       "H2O_cluster_timezone:       Asia/Kolkata\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.30.0.1\n",
       "H2O_cluster_version_age:    1 month and 14 days\n",
       "H2O_cluster_name:           H2O_from_python_niran_u90zos\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    3.505 Gb\n",
       "H2O_cluster_total_cores:    8\n",
       "H2O_cluster_allowed_cores:  8\n",
       "H2O_cluster_status:         accepting new members, healthy\n",
       "H2O_connection_url:         http://127.0.0.1:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "H2O_API_Extensions:         Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python_version:             3.7.4 final\n",
       "--------------------------  ---------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.automl import H2OAutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "df = h2o.import_file('Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "testing_df = h2o.import_file('Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:543\n",
      "Cols:8\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>City              </th><th>Location_Score    </th><th>Internal_Audit_Score  </th><th>External_Audit_Score  </th><th>Fin_Score         </th><th>Loss_score       </th><th>Past_Results      </th><th>IsUnderRisk        </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int               </td><td>real              </td><td>int                   </td><td>int                   </td><td>int               </td><td>int              </td><td>int               </td><td>int                </td></tr>\n",
       "<tr><td>mins   </td><td>0.0               </td><td>5.185             </td><td>3.0                   </td><td>3.0                   </td><td>3.0               </td><td>3.0              </td><td>0.0               </td><td>0.0                </td></tr>\n",
       "<tr><td>mean   </td><td>19.57642725598529 </td><td>32.25934622467772 </td><td>8.189686924493541     </td><td>7.3278084714548815    </td><td>7.046040515653774 </td><td>5.530386740331486</td><td>0.6132596685082867</td><td>0.6261510128913443 </td></tr>\n",
       "<tr><td>maxs   </td><td>44.0              </td><td>80.809            </td><td>15.0                  </td><td>15.0                  </td><td>15.0              </td><td>13.0             </td><td>10.0              </td><td>1.0                </td></tr>\n",
       "<tr><td>sigma  </td><td>14.722687406901029</td><td>24.887291329536744</td><td>3.3120216696421636    </td><td>3.450667108100174     </td><td>3.1569781510688233</td><td>1.839124119093846</td><td>0.7645085370985525</td><td>0.48427039429579793</td></tr>\n",
       "<tr><td>zeros  </td><td>8                 </td><td>0                 </td><td>0                     </td><td>0                     </td><td>0                 </td><td>0                </td><td>250               </td><td>203                </td></tr>\n",
       "<tr><td>missing</td><td>0                 </td><td>0                 </td><td>0                     </td><td>0                     </td><td>0                 </td><td>0                </td><td>0                 </td><td>0                  </td></tr>\n",
       "<tr><td>0      </td><td>2.0               </td><td>8.032             </td><td>14.0                  </td><td>8.0                   </td><td>3.0               </td><td>6.0              </td><td>0.0               </td><td>1.0                </td></tr>\n",
       "<tr><td>1      </td><td>31.0              </td><td>77.73             </td><td>8.0                   </td><td>3.0                   </td><td>3.0               </td><td>8.0              </td><td>1.0               </td><td>0.0                </td></tr>\n",
       "<tr><td>2      </td><td>40.0              </td><td>59.203            </td><td>3.0                   </td><td>12.0                  </td><td>11.0              </td><td>3.0              </td><td>0.0               </td><td>1.0                </td></tr>\n",
       "<tr><td>3      </td><td>12.0              </td><td>73.08             </td><td>4.0                   </td><td>5.0                   </td><td>7.0               </td><td>6.0              </td><td>0.0               </td><td>0.0                </td></tr>\n",
       "<tr><td>4      </td><td>4.0               </td><td>15.666            </td><td>13.0                  </td><td>15.0                  </td><td>6.0               </td><td>7.0              </td><td>2.0               </td><td>1.0                </td></tr>\n",
       "<tr><td>5      </td><td>1.0               </td><td>6.237             </td><td>10.0                  </td><td>10.0                  </td><td>12.0              </td><td>3.0              </td><td>1.0               </td><td>1.0                </td></tr>\n",
       "<tr><td>6      </td><td>9.0               </td><td>13.795            </td><td>8.0                   </td><td>3.0                   </td><td>5.0               </td><td>3.0              </td><td>0.0               </td><td>0.0                </td></tr>\n",
       "<tr><td>7      </td><td>23.0              </td><td>74.132            </td><td>11.0                  </td><td>15.0                  </td><td>5.0               </td><td>8.0              </td><td>0.0               </td><td>1.0                </td></tr>\n",
       "<tr><td>8      </td><td>40.0              </td><td>69.522            </td><td>8.0                   </td><td>4.0                   </td><td>7.0               </td><td>6.0              </td><td>0.0               </td><td>0.0                </td></tr>\n",
       "<tr><td>9      </td><td>38.0              </td><td>6.577             </td><td>8.0                   </td><td>5.0                   </td><td>7.0               </td><td>3.0              </td><td>1.0               </td><td>0.0                </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since this is classification problem, converting target as factor\n",
    "df['IsUnderRisk'] = df['IsUnderRisk'].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  IsUnderRisk</th><th style=\"text-align: right;\">  Count</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">            0</td><td style=\"text-align: right;\">    203</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            1</td><td style=\"text-align: right;\">    340</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['IsUnderRisk'].table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:543\n",
      "Cols:8\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>City              </th><th>Location_Score    </th><th>Internal_Audit_Score  </th><th>External_Audit_Score  </th><th>Fin_Score         </th><th>Loss_score       </th><th>Past_Results      </th><th>IsUnderRisk  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int               </td><td>real              </td><td>int                   </td><td>int                   </td><td>int               </td><td>int              </td><td>int               </td><td>enum         </td></tr>\n",
       "<tr><td>mins   </td><td>0.0               </td><td>5.185             </td><td>3.0                   </td><td>3.0                   </td><td>3.0               </td><td>3.0              </td><td>0.0               </td><td>             </td></tr>\n",
       "<tr><td>mean   </td><td>19.57642725598529 </td><td>32.25934622467772 </td><td>8.189686924493541     </td><td>7.3278084714548815    </td><td>7.046040515653774 </td><td>5.530386740331486</td><td>0.6132596685082867</td><td>             </td></tr>\n",
       "<tr><td>maxs   </td><td>44.0              </td><td>80.809            </td><td>15.0                  </td><td>15.0                  </td><td>15.0              </td><td>13.0             </td><td>10.0              </td><td>             </td></tr>\n",
       "<tr><td>sigma  </td><td>14.722687406901029</td><td>24.887291329536744</td><td>3.3120216696421636    </td><td>3.450667108100174     </td><td>3.1569781510688233</td><td>1.839124119093846</td><td>0.7645085370985525</td><td>             </td></tr>\n",
       "<tr><td>zeros  </td><td>8                 </td><td>0                 </td><td>0                     </td><td>0                     </td><td>0                 </td><td>0                </td><td>250               </td><td>             </td></tr>\n",
       "<tr><td>missing</td><td>0                 </td><td>0                 </td><td>0                     </td><td>0                     </td><td>0                 </td><td>0                </td><td>0                 </td><td>0            </td></tr>\n",
       "<tr><td>0      </td><td>2.0               </td><td>8.032             </td><td>14.0                  </td><td>8.0                   </td><td>3.0               </td><td>6.0              </td><td>0.0               </td><td>1            </td></tr>\n",
       "<tr><td>1      </td><td>31.0              </td><td>77.73             </td><td>8.0                   </td><td>3.0                   </td><td>3.0               </td><td>8.0              </td><td>1.0               </td><td>0            </td></tr>\n",
       "<tr><td>2      </td><td>40.0              </td><td>59.203            </td><td>3.0                   </td><td>12.0                  </td><td>11.0              </td><td>3.0              </td><td>0.0               </td><td>1            </td></tr>\n",
       "<tr><td>3      </td><td>12.0              </td><td>73.08             </td><td>4.0                   </td><td>5.0                   </td><td>7.0               </td><td>6.0              </td><td>0.0               </td><td>0            </td></tr>\n",
       "<tr><td>4      </td><td>4.0               </td><td>15.666            </td><td>13.0                  </td><td>15.0                  </td><td>6.0               </td><td>7.0              </td><td>2.0               </td><td>1            </td></tr>\n",
       "<tr><td>5      </td><td>1.0               </td><td>6.237             </td><td>10.0                  </td><td>10.0                  </td><td>12.0              </td><td>3.0              </td><td>1.0               </td><td>1            </td></tr>\n",
       "<tr><td>6      </td><td>9.0               </td><td>13.795            </td><td>8.0                   </td><td>3.0                   </td><td>5.0               </td><td>3.0              </td><td>0.0               </td><td>0            </td></tr>\n",
       "<tr><td>7      </td><td>23.0              </td><td>74.132            </td><td>11.0                  </td><td>15.0                  </td><td>5.0               </td><td>8.0              </td><td>0.0               </td><td>1            </td></tr>\n",
       "<tr><td>8      </td><td>40.0              </td><td>69.522            </td><td>8.0                   </td><td>4.0                   </td><td>7.0               </td><td>6.0              </td><td>0.0               </td><td>0            </td></tr>\n",
       "<tr><td>9      </td><td>38.0              </td><td>6.577             </td><td>8.0                   </td><td>5.0                   </td><td>7.0               </td><td>3.0              </td><td>1.0               </td><td>0            </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test,valid = df.split_frame(ratios=[.85,0.10],seed=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = \"IsUnderRisk\"\n",
    "x = df.columns\n",
    "x.remove(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'IsUnderRisk'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "aml = H2OAutoML(    nfolds=10,\n",
    "    balance_classes=True,\n",
    "    class_sampling_factors=None,\n",
    "    max_after_balance_size=5.0,\n",
    "    max_runtime_secs=None,\n",
    "    max_runtime_secs_per_model=None,\n",
    "    max_models=None,\n",
    "    stopping_metric='AUTO',\n",
    "    stopping_tolerance=None,\n",
    "    stopping_rounds=None,\n",
    "    seed=4,\n",
    "    project_name=None,\n",
    "    exclude_algos=['DeepLearning'],\n",
    "    include_algos=None,\n",
    "    exploitation_ratio=0,\n",
    "    modeling_plan=None,\n",
    "    monotone_constraints=None,\n",
    "    algo_parameters=None,\n",
    "    keep_cross_validation_predictions=True,\n",
    "    keep_cross_validation_models=False,\n",
    "    keep_cross_validation_fold_assignment=True,\n",
    "    sort_metric='logloss',\n",
    "    export_checkpoints_dir=None,\n",
    "    verbosity='warn',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |\n",
      "15:27:54.123: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.\n",
      "15:27:54.123: AutoML: XGBoost is not available; skipping it.\n",
      "\n",
      "████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "aml.train(x = x, y = y, training_frame = train, validation_frame=valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_8         </td><td style=\"text-align: right;\"> 0.273228</td><td style=\"text-align: right;\">0.944684</td><td style=\"text-align: right;\">0.972429</td><td style=\"text-align: right;\">              0.114164</td><td style=\"text-align: right;\">0.294122</td><td style=\"text-align: right;\">0.0865078</td></tr>\n",
       "<tr><td>GBM_1_AutoML_20200518_152754                       </td><td style=\"text-align: right;\"> 0.286105</td><td style=\"text-align: right;\">0.941301</td><td style=\"text-align: right;\">0.971328</td><td style=\"text-align: right;\">              0.106741</td><td style=\"text-align: right;\">0.300823</td><td style=\"text-align: right;\">0.0904944</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_2         </td><td style=\"text-align: right;\"> 0.288746</td><td style=\"text-align: right;\">0.939925</td><td style=\"text-align: right;\">0.970185</td><td style=\"text-align: right;\">              0.133681</td><td style=\"text-align: right;\">0.303286</td><td style=\"text-align: right;\">0.0919824</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_14        </td><td style=\"text-align: right;\"> 0.29324 </td><td style=\"text-align: right;\">0.935993</td><td style=\"text-align: right;\">0.96863 </td><td style=\"text-align: right;\">              0.114763</td><td style=\"text-align: right;\">0.305187</td><td style=\"text-align: right;\">0.0931392</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_3         </td><td style=\"text-align: right;\"> 0.29623 </td><td style=\"text-align: right;\">0.933629</td><td style=\"text-align: right;\">0.967713</td><td style=\"text-align: right;\">              0.115362</td><td style=\"text-align: right;\">0.306272</td><td style=\"text-align: right;\">0.0938025</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_4         </td><td style=\"text-align: right;\"> 0.296268</td><td style=\"text-align: right;\">0.937221</td><td style=\"text-align: right;\">0.968557</td><td style=\"text-align: right;\">              0.106861</td><td style=\"text-align: right;\">0.307745</td><td style=\"text-align: right;\">0.0947068</td></tr>\n",
       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20200518_152754</td><td style=\"text-align: right;\"> 0.296368</td><td style=\"text-align: right;\">0.939156</td><td style=\"text-align: right;\">0.970525</td><td style=\"text-align: right;\">              0.114164</td><td style=\"text-align: right;\">0.303331</td><td style=\"text-align: right;\">0.0920094</td></tr>\n",
       "<tr><td>StackedEnsemble_AllModels_AutoML_20200518_152754   </td><td style=\"text-align: right;\"> 0.303219</td><td style=\"text-align: right;\">0.9376  </td><td style=\"text-align: right;\">0.969896</td><td style=\"text-align: right;\">              0.122785</td><td style=\"text-align: right;\">0.305185</td><td style=\"text-align: right;\">0.0931381</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_17        </td><td style=\"text-align: right;\"> 0.30434 </td><td style=\"text-align: right;\">0.932421</td><td style=\"text-align: right;\">0.966949</td><td style=\"text-align: right;\">              0.12518 </td><td style=\"text-align: right;\">0.313264</td><td style=\"text-align: right;\">0.0981344</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200518_152754_model_9         </td><td style=\"text-align: right;\"> 0.305041</td><td style=\"text-align: right;\">0.938857</td><td style=\"text-align: right;\">0.969137</td><td style=\"text-align: right;\">              0.109016</td><td style=\"text-align: right;\">0.311429</td><td style=\"text-align: right;\">0.0969882</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb = aml.leaderboard\n",
    "lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see Gradient boosting machine performed well in reducing logloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "pred_df = aml.leader.predict(testing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  GBM_grid__1_AutoML_20200518_152754_model_8\n",
      "\n",
      "\n",
      "Model Summary: \n"
     ]
    },
    {
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       "      <th>number_of_trees</th>\n",
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       "      <td>0</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7721.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "     number_of_trees  number_of_internal_trees  model_size_in_bytes  \\\n",
       "0               57.0                      57.0               7721.0   \n",
       "\n",
       "   min_depth  max_depth  mean_depth  min_leaves  max_leaves  mean_leaves  \n",
       "0        3.0        3.0         3.0         4.0         8.0     6.122807  "
      ]
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     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.05409967703907445\n",
      "RMSE: 0.23259337273248878\n",
      "LogLoss: 0.2024404851580308\n",
      "Mean Per-Class Error: 0.05724961553248753\n",
      "AUC: 0.9828371299500192\n",
      "AUCPR: 0.9864511728706904\n",
      "Gini: 0.9656742599000383\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.35919605611511823: \n"
     ]
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>(25.0/289.0)</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>(10.0/288.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Total</td>\n",
       "      <td>274.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>0.0607</td>\n",
       "      <td>(35.0/577.0)</td>\n",
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       "              0      1   Error           Rate\n",
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       "1      1   10.0  278.0  0.0347   (10.0/288.0)\n",
       "2  Total  274.0  303.0  0.0607   (35.0/577.0)"
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     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
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       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
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       "      <th>idx</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>max f1</td>\n",
       "      <td>0.359196</td>\n",
       "      <td>0.940778</td>\n",
       "      <td>248.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>max f2</td>\n",
       "      <td>0.359196</td>\n",
       "      <td>0.955326</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.684102</td>\n",
       "      <td>0.973926</td>\n",
       "      <td>209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.603820</td>\n",
       "      <td>0.942808</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>max precision</td>\n",
       "      <td>0.997575</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>max recall</td>\n",
       "      <td>0.128362</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.997575</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.603820</td>\n",
       "      <td>0.888428</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.496127</td>\n",
       "      <td>0.930796</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.588094</td>\n",
       "      <td>0.942750</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>max tns</td>\n",
       "      <td>0.997575</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>max fns</td>\n",
       "      <td>0.997575</td>\n",
       "      <td>287.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>max fps</td>\n",
       "      <td>0.055665</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>max tps</td>\n",
       "      <td>0.128362</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.997575</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.997575</td>\n",
       "      <td>0.996528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.055665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.128362</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold       value    idx\n",
       "0                        max f1   0.359196    0.940778  248.0\n",
       "1                        max f2   0.359196    0.955326  248.0\n",
       "2                  max f0point5   0.684102    0.973926  209.0\n",
       "3                  max accuracy   0.603820    0.942808  217.0\n",
       "4                 max precision   0.997575    1.000000    0.0\n",
       "5                    max recall   0.128362    1.000000  355.0\n",
       "6               max specificity   0.997575    1.000000    0.0\n",
       "7              max absolute_mcc   0.603820    0.888428  217.0\n",
       "8    max min_per_class_accuracy   0.496127    0.930796  235.0\n",
       "9   max mean_per_class_accuracy   0.588094    0.942750  220.0\n",
       "10                      max tns   0.997575  289.000000    0.0\n",
       "11                      max fns   0.997575  287.000000    0.0\n",
       "12                      max fps   0.055665  289.000000  399.0\n",
       "13                      max tps   0.128362  288.000000  355.0\n",
       "14                      max tnr   0.997575    1.000000    0.0\n",
       "15                      max fnr   0.997575    0.996528    0.0\n",
       "16                      max fpr   0.055665    1.000000  399.0\n",
       "17                      max tpr   0.128362    1.000000  355.0"
      ]
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     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 49.91 %, avg score: 54.79 %\n"
     ]
    },
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       "      <th>group</th>\n",
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       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.010399</td>\n",
       "      <td>0.995310</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996398</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.020833</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
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       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.020797</td>\n",
       "      <td>0.993420</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995280</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.993229</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994618</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.045061</td>\n",
       "      <td>0.992946</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993058</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994138</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.090278</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.050260</td>\n",
       "      <td>0.992590</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993995</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>0.100694</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.100520</td>\n",
       "      <td>0.990958</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992908</td>\n",
       "      <td>0.100694</td>\n",
       "      <td>0.201389</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.150780</td>\n",
       "      <td>0.989491</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992041</td>\n",
       "      <td>0.100694</td>\n",
       "      <td>0.302083</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.201040</td>\n",
       "      <td>0.987618</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991199</td>\n",
       "      <td>0.100694</td>\n",
       "      <td>0.402778</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.299827</td>\n",
       "      <td>0.978455</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984128</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988870</td>\n",
       "      <td>0.197917</td>\n",
       "      <td>0.600694</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.400347</td>\n",
       "      <td>0.813857</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974547</td>\n",
       "      <td>0.201389</td>\n",
       "      <td>0.802083</td>\n",
       "      <td>100.347222</td>\n",
       "      <td>100.347222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.500867</td>\n",
       "      <td>0.496127</td>\n",
       "      <td>1.312620</td>\n",
       "      <td>1.864824</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.639509</td>\n",
       "      <td>0.930796</td>\n",
       "      <td>0.907307</td>\n",
       "      <td>0.131944</td>\n",
       "      <td>0.934028</td>\n",
       "      <td>31.261973</td>\n",
       "      <td>86.482363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.599653</td>\n",
       "      <td>0.257438</td>\n",
       "      <td>0.386635</td>\n",
       "      <td>1.621307</td>\n",
       "      <td>0.192982</td>\n",
       "      <td>0.325623</td>\n",
       "      <td>0.809249</td>\n",
       "      <td>0.811481</td>\n",
       "      <td>0.038194</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>-61.336501</td>\n",
       "      <td>62.130700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.700173</td>\n",
       "      <td>0.199338</td>\n",
       "      <td>0.172713</td>\n",
       "      <td>1.413341</td>\n",
       "      <td>0.086207</td>\n",
       "      <td>0.228181</td>\n",
       "      <td>0.705446</td>\n",
       "      <td>0.727740</td>\n",
       "      <td>0.017361</td>\n",
       "      <td>0.989583</td>\n",
       "      <td>-82.728688</td>\n",
       "      <td>41.334055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.798960</td>\n",
       "      <td>0.149654</td>\n",
       "      <td>0.070297</td>\n",
       "      <td>1.247281</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>0.174319</td>\n",
       "      <td>0.622560</td>\n",
       "      <td>0.659312</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.996528</td>\n",
       "      <td>-92.970273</td>\n",
       "      <td>24.728097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.901213</td>\n",
       "      <td>0.111336</td>\n",
       "      <td>0.033957</td>\n",
       "      <td>1.109615</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.128296</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.599062</td>\n",
       "      <td>0.003472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-96.604284</td>\n",
       "      <td>10.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055665</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080770</td>\n",
       "      <td>0.499133</td>\n",
       "      <td>0.547862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                  0.010399         0.995310  2.003472   \n",
       "1         2                  0.020797         0.993420  2.003472   \n",
       "2         3                  0.031196         0.993229  2.003472   \n",
       "3         4                  0.045061         0.992946  2.003472   \n",
       "4         5                  0.050260         0.992590  2.003472   \n",
       "5         6                  0.100520         0.990958  2.003472   \n",
       "6         7                  0.150780         0.989491  2.003472   \n",
       "7         8                  0.201040         0.987618  2.003472   \n",
       "8         9                  0.299827         0.978455  2.003472   \n",
       "9        10                  0.400347         0.813857  2.003472   \n",
       "10       11                  0.500867         0.496127  1.312620   \n",
       "11       12                  0.599653         0.257438  0.386635   \n",
       "12       13                  0.700173         0.199338  0.172713   \n",
       "13       14                  0.798960         0.149654  0.070297   \n",
       "14       15                  0.901213         0.111336  0.033957   \n",
       "15       16                  1.000000         0.055665  0.000000   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          2.003472       1.000000  0.996398                  1.000000   \n",
       "1          2.003472       1.000000  0.994162                  1.000000   \n",
       "2          2.003472       1.000000  0.993294                  1.000000   \n",
       "3          2.003472       1.000000  0.993058                  1.000000   \n",
       "4          2.003472       1.000000  0.992754                  1.000000   \n",
       "5          2.003472       1.000000  0.991822                  1.000000   \n",
       "6          2.003472       1.000000  0.990305                  1.000000   \n",
       "7          2.003472       1.000000  0.988676                  1.000000   \n",
       "8          2.003472       1.000000  0.984128                  1.000000   \n",
       "9          2.003472       1.000000  0.931824                  1.000000   \n",
       "10         1.864824       0.655172  0.639509                  0.930796   \n",
       "11         1.621307       0.192982  0.325623                  0.809249   \n",
       "12         1.413341       0.086207  0.228181                  0.705446   \n",
       "13         1.247281       0.035088  0.174319                  0.622560   \n",
       "14         1.109615       0.016949  0.128296                  0.553846   \n",
       "15         1.000000       0.000000  0.080770                  0.499133   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.996398      0.020833                 0.020833  100.347222   \n",
       "1           0.995280      0.020833                 0.041667  100.347222   \n",
       "2           0.994618      0.020833                 0.062500  100.347222   \n",
       "3           0.994138      0.027778                 0.090278  100.347222   \n",
       "4           0.993995      0.010417                 0.100694  100.347222   \n",
       "5           0.992908      0.100694                 0.201389  100.347222   \n",
       "6           0.992041      0.100694                 0.302083  100.347222   \n",
       "7           0.991199      0.100694                 0.402778  100.347222   \n",
       "8           0.988870      0.197917                 0.600694  100.347222   \n",
       "9           0.974547      0.201389                 0.802083  100.347222   \n",
       "10          0.907307      0.131944                 0.934028   31.261973   \n",
       "11          0.811481      0.038194                 0.972222  -61.336501   \n",
       "12          0.727740      0.017361                 0.989583  -82.728688   \n",
       "13          0.659312      0.006944                 0.996528  -92.970273   \n",
       "14          0.599062      0.003472                 1.000000  -96.604284   \n",
       "15          0.547862      0.000000                 1.000000 -100.000000   \n",
       "\n",
       "    cumulative_gain  \n",
       "0        100.347222  \n",
       "1        100.347222  \n",
       "2        100.347222  \n",
       "3        100.347222  \n",
       "4        100.347222  \n",
       "5        100.347222  \n",
       "6        100.347222  \n",
       "7        100.347222  \n",
       "8        100.347222  \n",
       "9        100.347222  \n",
       "10        86.482363  \n",
       "11        62.130700  \n",
       "12        41.334055  \n",
       "13        24.728097  \n",
       "14        10.961538  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.07636212669403694\n",
      "RMSE: 0.2763369803230052\n",
      "LogLoss: 0.24252917589879183\n",
      "Mean Per-Class Error: 0.08823529411764708\n",
      "AUC: 0.9632352941176471\n",
      "AUCPR: 0.983585704346079\n",
      "Gini: 0.9264705882352942\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4295998894908135: \n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>(1.0/8.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>(1.0/17.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Total</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>(2.0/25.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0     1   Error         Rate\n",
       "0      0  7.0   1.0   0.125    (1.0/8.0)\n",
       "1      1  1.0  16.0  0.0588   (1.0/17.0)\n",
       "2  Total  8.0  17.0    0.08   (2.0/25.0)"
      ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>max f1</td>\n",
       "      <td>0.429600</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>max f2</td>\n",
       "      <td>0.205967</td>\n",
       "      <td>0.965909</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.563967</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.429600</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>max precision</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>max recall</td>\n",
       "      <td>0.205967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.429600</td>\n",
       "      <td>0.816176</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.465317</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.563967</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>max tns</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>max fns</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>max fps</td>\n",
       "      <td>0.097211</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>max tps</td>\n",
       "      <td>0.205967</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.097211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.205967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold      value   idx\n",
       "0                        max f1   0.429600   0.941176  16.0\n",
       "1                        max f2   0.205967   0.965909  19.0\n",
       "2                  max f0point5   0.563967   0.958904  13.0\n",
       "3                  max accuracy   0.429600   0.920000  16.0\n",
       "4                 max precision   0.996216   1.000000   0.0\n",
       "5                    max recall   0.205967   1.000000  19.0\n",
       "6               max specificity   0.996216   1.000000   0.0\n",
       "7              max absolute_mcc   0.429600   0.816176  16.0\n",
       "8    max min_per_class_accuracy   0.465317   0.875000  15.0\n",
       "9   max mean_per_class_accuracy   0.563967   0.911765  13.0\n",
       "10                      max tns   0.996216   8.000000   0.0\n",
       "11                      max fns   0.996216  16.000000   0.0\n",
       "12                      max fps   0.097211   8.000000  24.0\n",
       "13                      max tps   0.205967  17.000000  19.0\n",
       "14                      max tnr   0.996216   1.000000   0.0\n",
       "15                      max fnr   0.996216   0.941176   0.0\n",
       "16                      max fpr   0.097211   1.000000  24.0\n",
       "17                      max tpr   0.205967   1.000000  19.0"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 68.00 %, avg score: 63.52 %\n"
     ]
    },
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>cumulative_data_fraction</th>\n",
       "      <th>lower_threshold</th>\n",
       "      <th>lift</th>\n",
       "      <th>cumulative_lift</th>\n",
       "      <th>response_rate</th>\n",
       "      <th>score</th>\n",
       "      <th>cumulative_response_rate</th>\n",
       "      <th>cumulative_score</th>\n",
       "      <th>capture_rate</th>\n",
       "      <th>cumulative_capture_rate</th>\n",
       "      <th>gain</th>\n",
       "      <th>cumulative_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.995177</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.994139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.993100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.992061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.991309</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994052</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.986268</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992367</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.981137</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989819</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.979956</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987944</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.971312</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983793</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.956689</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967249</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980484</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.928080</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.940910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971352</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>47.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.507957</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>1.372549</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.550175</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.915195</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>-26.470588</td>\n",
       "      <td>37.254902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.271563</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>1.384083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.447458</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.860167</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>47.058824</td>\n",
       "      <td>38.408304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.194900</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.215922</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.763530</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-50.980392</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.126871</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.139242</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.706777</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>13.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.097211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110584</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.635234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                      0.04         0.995177  1.470588   \n",
       "1         2                      0.04         0.994139  0.000000   \n",
       "2         3                      0.04         0.993100  0.000000   \n",
       "3         4                      0.04         0.992061  0.000000   \n",
       "4         5                      0.08         0.991309  1.470588   \n",
       "5         6                      0.12         0.986268  1.470588   \n",
       "6         7                      0.16         0.981137  1.470588   \n",
       "7         8                      0.20         0.979956  1.470588   \n",
       "8         9                      0.32         0.971312  1.470588   \n",
       "9        10                      0.40         0.956689  1.470588   \n",
       "10       11                      0.52         0.928080  1.470588   \n",
       "11       12                      0.60         0.507957  0.735294   \n",
       "12       13                      0.68         0.271563  1.470588   \n",
       "13       14                      0.80         0.194900  0.490196   \n",
       "14       15                      0.88         0.126871  0.000000   \n",
       "15       16                      1.00         0.097211  0.000000   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          1.470588       1.000000  0.996216                  1.000000   \n",
       "1          1.470588       0.000000  0.000000                  1.000000   \n",
       "2          1.470588       0.000000  0.000000                  1.000000   \n",
       "3          1.470588       0.000000  0.000000                  1.000000   \n",
       "4          1.470588       1.000000  0.991888                  1.000000   \n",
       "5          1.470588       1.000000  0.988996                  1.000000   \n",
       "6          1.470588       1.000000  0.982177                  1.000000   \n",
       "7          1.470588       1.000000  0.980444                  1.000000   \n",
       "8          1.470588       1.000000  0.976874                  1.000000   \n",
       "9          1.470588       1.000000  0.967249                  1.000000   \n",
       "10         1.470588       1.000000  0.940910                  1.000000   \n",
       "11         1.372549       0.500000  0.550175                  0.933333   \n",
       "12         1.384083       1.000000  0.447458                  0.941176   \n",
       "13         1.250000       0.333333  0.215922                  0.850000   \n",
       "14         1.136364       0.000000  0.139242                  0.772727   \n",
       "15         1.000000       0.000000  0.110584                  0.680000   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.996216      0.058824                 0.058824   47.058824   \n",
       "1           0.996216      0.000000                 0.058824 -100.000000   \n",
       "2           0.996216      0.000000                 0.058824 -100.000000   \n",
       "3           0.996216      0.000000                 0.058824 -100.000000   \n",
       "4           0.994052      0.058824                 0.117647   47.058824   \n",
       "5           0.992367      0.058824                 0.176471   47.058824   \n",
       "6           0.989819      0.058824                 0.235294   47.058824   \n",
       "7           0.987944      0.058824                 0.294118   47.058824   \n",
       "8           0.983793      0.176471                 0.470588   47.058824   \n",
       "9           0.980484      0.117647                 0.588235   47.058824   \n",
       "10          0.971352      0.176471                 0.764706   47.058824   \n",
       "11          0.915195      0.058824                 0.823529  -26.470588   \n",
       "12          0.860167      0.117647                 0.941176   47.058824   \n",
       "13          0.763530      0.058824                 1.000000  -50.980392   \n",
       "14          0.706777      0.000000                 1.000000 -100.000000   \n",
       "15          0.635234      0.000000                 1.000000 -100.000000   \n",
       "\n",
       "    cumulative_gain  \n",
       "0         47.058824  \n",
       "1         47.058824  \n",
       "2         47.058824  \n",
       "3         47.058824  \n",
       "4         47.058824  \n",
       "5         47.058824  \n",
       "6         47.058824  \n",
       "7         47.058824  \n",
       "8         47.058824  \n",
       "9         47.058824  \n",
       "10        47.058824  \n",
       "11        37.254902  \n",
       "12        38.408304  \n",
       "13        25.000000  \n",
       "14        13.636364  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 0.08650776484001016\n",
      "RMSE: 0.2941220237248652\n",
      "LogLoss: 0.27322760086380427\n",
      "Mean Per-Class Error: 0.10237068965517238\n",
      "AUC: 0.944683908045977\n",
      "AUCPR: 0.9724290425921714\n",
      "Gini: 0.889367816091954\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4894744507515046: \n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.1207</td>\n",
       "      <td>(21.0/174.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>0.1076</td>\n",
       "      <td>(31.0/288.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Total</td>\n",
       "      <td>184.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>(52.0/462.0)</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "              0      1   Error           Rate\n",
       "0      0  153.0   21.0  0.1207   (21.0/174.0)\n",
       "1      1   31.0  257.0  0.1076   (31.0/288.0)\n",
       "2  Total  184.0  278.0  0.1126   (52.0/462.0)"
      ]
     },
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>max f1</td>\n",
       "      <td>0.489474</td>\n",
       "      <td>0.908127</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>max f2</td>\n",
       "      <td>0.196256</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.747910</td>\n",
       "      <td>0.948845</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.539556</td>\n",
       "      <td>0.887446</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>max precision</td>\n",
       "      <td>0.998409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>max recall</td>\n",
       "      <td>0.070045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.998409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.712984</td>\n",
       "      <td>0.770928</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.511212</td>\n",
       "      <td>0.885057</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.712984</td>\n",
       "      <td>0.897629</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>max tns</td>\n",
       "      <td>0.998409</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>max fns</td>\n",
       "      <td>0.998409</td>\n",
       "      <td>287.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>max fps</td>\n",
       "      <td>0.053012</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>max tps</td>\n",
       "      <td>0.070045</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.998409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.998409</td>\n",
       "      <td>0.996528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.053012</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.070045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold       value    idx\n",
       "0                        max f1   0.489474    0.908127  220.0\n",
       "1                        max f2   0.196256    0.921053  308.0\n",
       "2                  max f0point5   0.747910    0.948845  176.0\n",
       "3                  max accuracy   0.539556    0.887446  214.0\n",
       "4                 max precision   0.998409    1.000000    0.0\n",
       "5                    max recall   0.070045    1.000000  392.0\n",
       "6               max specificity   0.998409    1.000000    0.0\n",
       "7              max absolute_mcc   0.712984    0.770928  182.0\n",
       "8    max min_per_class_accuracy   0.511212    0.885057  217.0\n",
       "9   max mean_per_class_accuracy   0.712984    0.897629  182.0\n",
       "10                      max tns   0.998409  174.000000    0.0\n",
       "11                      max fns   0.998409  287.000000    0.0\n",
       "12                      max fps   0.053012  174.000000  399.0\n",
       "13                      max tps   0.070045  288.000000  392.0\n",
       "14                      max tnr   0.998409    1.000000    0.0\n",
       "15                      max fnr   0.998409    0.996528    0.0\n",
       "16                      max fpr   0.053012    1.000000  399.0\n",
       "17                      max tpr   0.070045    1.000000  392.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 62.34 %, avg score: 63.16 %\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>cumulative_data_fraction</th>\n",
       "      <th>lower_threshold</th>\n",
       "      <th>lift</th>\n",
       "      <th>cumulative_lift</th>\n",
       "      <th>response_rate</th>\n",
       "      <th>score</th>\n",
       "      <th>cumulative_response_rate</th>\n",
       "      <th>cumulative_score</th>\n",
       "      <th>capture_rate</th>\n",
       "      <th>cumulative_capture_rate</th>\n",
       "      <th>gain</th>\n",
       "      <th>cumulative_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.010823</td>\n",
       "      <td>0.996665</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997193</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.021645</td>\n",
       "      <td>0.996043</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996718</td>\n",
       "      <td>0.017361</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.995456</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996445</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.048611</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.041126</td>\n",
       "      <td>0.994885</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996090</td>\n",
       "      <td>0.017361</td>\n",
       "      <td>0.065972</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>0.994325</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>0.017361</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>0.993148</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994736</td>\n",
       "      <td>0.079861</td>\n",
       "      <td>0.163194</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.991999</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992451</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993986</td>\n",
       "      <td>0.079861</td>\n",
       "      <td>0.243056</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.201299</td>\n",
       "      <td>0.990727</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993335</td>\n",
       "      <td>0.079861</td>\n",
       "      <td>0.322917</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.300866</td>\n",
       "      <td>0.984731</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991623</td>\n",
       "      <td>0.159722</td>\n",
       "      <td>0.482639</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.400433</td>\n",
       "      <td>0.968771</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.604167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988647</td>\n",
       "      <td>0.159722</td>\n",
       "      <td>0.642361</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>60.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.746701</td>\n",
       "      <td>1.569293</td>\n",
       "      <td>1.597222</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.883266</td>\n",
       "      <td>0.995671</td>\n",
       "      <td>0.967662</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.798611</td>\n",
       "      <td>56.929348</td>\n",
       "      <td>59.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.599567</td>\n",
       "      <td>0.491826</td>\n",
       "      <td>0.906703</td>\n",
       "      <td>1.482551</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.630280</td>\n",
       "      <td>0.924188</td>\n",
       "      <td>0.911634</td>\n",
       "      <td>0.090278</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>-9.329710</td>\n",
       "      <td>48.255114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.699134</td>\n",
       "      <td>0.284157</td>\n",
       "      <td>0.348732</td>\n",
       "      <td>1.321078</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.370141</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.834518</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>0.923611</td>\n",
       "      <td>-65.126812</td>\n",
       "      <td>32.107843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.798701</td>\n",
       "      <td>0.195230</td>\n",
       "      <td>0.488225</td>\n",
       "      <td>1.217254</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.239783</td>\n",
       "      <td>0.758808</td>\n",
       "      <td>0.760377</td>\n",
       "      <td>0.048611</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>-51.177536</td>\n",
       "      <td>21.725384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.898268</td>\n",
       "      <td>0.118571</td>\n",
       "      <td>0.139493</td>\n",
       "      <td>1.097791</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.152904</td>\n",
       "      <td>0.684337</td>\n",
       "      <td>0.693043</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>-86.050725</td>\n",
       "      <td>9.779116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053012</td>\n",
       "      <td>0.136525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.089145</td>\n",
       "      <td>0.623377</td>\n",
       "      <td>0.631607</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-86.347518</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                  0.010823         0.996665  1.604167   \n",
       "1         2                  0.021645         0.996043  1.604167   \n",
       "2         3                  0.030303         0.995456  1.604167   \n",
       "3         4                  0.041126         0.994885  1.604167   \n",
       "4         5                  0.051948         0.994325  1.604167   \n",
       "5         6                  0.101732         0.993148  1.604167   \n",
       "6         7                  0.151515         0.991999  1.604167   \n",
       "7         8                  0.201299         0.990727  1.604167   \n",
       "8         9                  0.300866         0.984731  1.604167   \n",
       "9        10                  0.400433         0.968771  1.604167   \n",
       "10       11                  0.500000         0.746701  1.569293   \n",
       "11       12                  0.599567         0.491826  0.906703   \n",
       "12       13                  0.699134         0.284157  0.348732   \n",
       "13       14                  0.798701         0.195230  0.488225   \n",
       "14       15                  0.898268         0.118571  0.139493   \n",
       "15       16                  1.000000         0.053012  0.136525   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          1.604167       1.000000  0.997193                  1.000000   \n",
       "1          1.604167       1.000000  0.996243                  1.000000   \n",
       "2          1.604167       1.000000  0.995762                  1.000000   \n",
       "3          1.604167       1.000000  0.995098                  1.000000   \n",
       "4          1.604167       1.000000  0.994643                  1.000000   \n",
       "5          1.604167       1.000000  0.993638                  1.000000   \n",
       "6          1.604167       1.000000  0.992451                  1.000000   \n",
       "7          1.604167       1.000000  0.991357                  1.000000   \n",
       "8          1.604167       1.000000  0.988160                  1.000000   \n",
       "9          1.604167       1.000000  0.979653                  1.000000   \n",
       "10         1.597222       0.978261  0.883266                  0.995671   \n",
       "11         1.482551       0.565217  0.630280                  0.924188   \n",
       "12         1.321078       0.217391  0.370141                  0.823529   \n",
       "13         1.217254       0.304348  0.239783                  0.758808   \n",
       "14         1.097791       0.086957  0.152904                  0.684337   \n",
       "15         1.000000       0.085106  0.089145                  0.623377   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate       gain  \\\n",
       "0           0.997193      0.017361                 0.017361  60.416667   \n",
       "1           0.996718      0.017361                 0.034722  60.416667   \n",
       "2           0.996445      0.013889                 0.048611  60.416667   \n",
       "3           0.996090      0.017361                 0.065972  60.416667   \n",
       "4           0.995789      0.017361                 0.083333  60.416667   \n",
       "5           0.994736      0.079861                 0.163194  60.416667   \n",
       "6           0.993986      0.079861                 0.243056  60.416667   \n",
       "7           0.993335      0.079861                 0.322917  60.416667   \n",
       "8           0.991623      0.159722                 0.482639  60.416667   \n",
       "9           0.988647      0.159722                 0.642361  60.416667   \n",
       "10          0.967662      0.156250                 0.798611  56.929348   \n",
       "11          0.911634      0.090278                 0.888889  -9.329710   \n",
       "12          0.834518      0.034722                 0.923611 -65.126812   \n",
       "13          0.760377      0.048611                 0.972222 -51.177536   \n",
       "14          0.693043      0.013889                 0.986111 -86.050725   \n",
       "15          0.631607      0.013889                 1.000000 -86.347518   \n",
       "\n",
       "    cumulative_gain  \n",
       "0         60.416667  \n",
       "1         60.416667  \n",
       "2         60.416667  \n",
       "3         60.416667  \n",
       "4         60.416667  \n",
       "5         60.416667  \n",
       "6         60.416667  \n",
       "7         60.416667  \n",
       "8         60.416667  \n",
       "9         60.416667  \n",
       "10        59.722222  \n",
       "11        48.255114  \n",
       "12        32.107843  \n",
       "13        21.725384  \n",
       "14         9.779116  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Cross-Validation Metrics Summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>cv_1_valid</th>\n",
       "      <th>cv_2_valid</th>\n",
       "      <th>cv_3_valid</th>\n",
       "      <th>cv_4_valid</th>\n",
       "      <th>cv_5_valid</th>\n",
       "      <th>cv_6_valid</th>\n",
       "      <th>cv_7_valid</th>\n",
       "      <th>cv_8_valid</th>\n",
       "      <th>cv_9_valid</th>\n",
       "      <th>cv_10_valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.9091582</td>\n",
       "      <td>0.037846405</td>\n",
       "      <td>0.89361703</td>\n",
       "      <td>0.89361703</td>\n",
       "      <td>0.9130435</td>\n",
       "      <td>0.95652175</td>\n",
       "      <td>0.95652175</td>\n",
       "      <td>0.82608694</td>\n",
       "      <td>0.9130435</td>\n",
       "      <td>0.9130435</td>\n",
       "      <td>0.9347826</td>\n",
       "      <td>0.8913044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>auc</td>\n",
       "      <td>0.9490777</td>\n",
       "      <td>0.026368229</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.9411765</td>\n",
       "      <td>0.94736844</td>\n",
       "      <td>0.97902095</td>\n",
       "      <td>0.99375</td>\n",
       "      <td>0.8968254</td>\n",
       "      <td>0.9479167</td>\n",
       "      <td>0.93560606</td>\n",
       "      <td>0.96344084</td>\n",
       "      <td>0.9356725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>aucpr</td>\n",
       "      <td>0.9728198</td>\n",
       "      <td>0.017460583</td>\n",
       "      <td>0.97514373</td>\n",
       "      <td>0.9732472</td>\n",
       "      <td>0.97362715</td>\n",
       "      <td>0.9925447</td>\n",
       "      <td>0.9967534</td>\n",
       "      <td>0.9425119</td>\n",
       "      <td>0.97766507</td>\n",
       "      <td>0.9481482</td>\n",
       "      <td>0.98478395</td>\n",
       "      <td>0.96377254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>err</td>\n",
       "      <td>0.090841815</td>\n",
       "      <td>0.037846405</td>\n",
       "      <td>0.10638298</td>\n",
       "      <td>0.10638298</td>\n",
       "      <td>0.08695652</td>\n",
       "      <td>0.04347826</td>\n",
       "      <td>0.04347826</td>\n",
       "      <td>0.17391305</td>\n",
       "      <td>0.08695652</td>\n",
       "      <td>0.08695652</td>\n",
       "      <td>0.06521739</td>\n",
       "      <td>0.10869565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>err_count</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.7511901</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>f0point5</td>\n",
       "      <td>0.936653</td>\n",
       "      <td>0.042016745</td>\n",
       "      <td>0.96153843</td>\n",
       "      <td>0.9246575</td>\n",
       "      <td>0.9259259</td>\n",
       "      <td>0.9872612</td>\n",
       "      <td>0.9493671</td>\n",
       "      <td>0.8333333</td>\n",
       "      <td>0.9507042</td>\n",
       "      <td>0.9574468</td>\n",
       "      <td>0.9602649</td>\n",
       "      <td>0.9160305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>f1</td>\n",
       "      <td>0.92409444</td>\n",
       "      <td>0.031948812</td>\n",
       "      <td>0.90909094</td>\n",
       "      <td>0.91525424</td>\n",
       "      <td>0.9259259</td>\n",
       "      <td>0.96875</td>\n",
       "      <td>0.9677419</td>\n",
       "      <td>0.8666667</td>\n",
       "      <td>0.9310345</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9508197</td>\n",
       "      <td>0.9056604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>f2</td>\n",
       "      <td>0.91328746</td>\n",
       "      <td>0.04084424</td>\n",
       "      <td>0.86206895</td>\n",
       "      <td>0.90604025</td>\n",
       "      <td>0.9259259</td>\n",
       "      <td>0.9509202</td>\n",
       "      <td>0.9868421</td>\n",
       "      <td>0.9027778</td>\n",
       "      <td>0.9121622</td>\n",
       "      <td>0.8490566</td>\n",
       "      <td>0.9415584</td>\n",
       "      <td>0.8955224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>lift_top_group</td>\n",
       "      <td>1.6218984</td>\n",
       "      <td>0.19049643</td>\n",
       "      <td>1.5666667</td>\n",
       "      <td>1.5666667</td>\n",
       "      <td>1.7037038</td>\n",
       "      <td>1.3939394</td>\n",
       "      <td>1.5333333</td>\n",
       "      <td>1.6428572</td>\n",
       "      <td>1.5333333</td>\n",
       "      <td>2.090909</td>\n",
       "      <td>1.483871</td>\n",
       "      <td>1.7037038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>logloss</td>\n",
       "      <td>0.27318785</td>\n",
       "      <td>0.07786334</td>\n",
       "      <td>0.2911743</td>\n",
       "      <td>0.2735675</td>\n",
       "      <td>0.2850713</td>\n",
       "      <td>0.15630236</td>\n",
       "      <td>0.16591774</td>\n",
       "      <td>0.41360745</td>\n",
       "      <td>0.2591859</td>\n",
       "      <td>0.35825554</td>\n",
       "      <td>0.2358886</td>\n",
       "      <td>0.29290783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>max_per_class_error</td>\n",
       "      <td>0.13681123</td>\n",
       "      <td>0.078728095</td>\n",
       "      <td>0.16666667</td>\n",
       "      <td>0.11764706</td>\n",
       "      <td>0.10526316</td>\n",
       "      <td>0.060606062</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.33333334</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.18181819</td>\n",
       "      <td>0.06666667</td>\n",
       "      <td>0.11111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>mcc</td>\n",
       "      <td>0.81240034</td>\n",
       "      <td>0.07810417</td>\n",
       "      <td>0.80245835</td>\n",
       "      <td>0.7733081</td>\n",
       "      <td>0.8206628</td>\n",
       "      <td>0.90229785</td>\n",
       "      <td>0.90571105</td>\n",
       "      <td>0.63134533</td>\n",
       "      <td>0.8173163</td>\n",
       "      <td>0.8374358</td>\n",
       "      <td>0.8551342</td>\n",
       "      <td>0.7783335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>mean_per_class_accuracy</td>\n",
       "      <td>0.9077053</td>\n",
       "      <td>0.045092028</td>\n",
       "      <td>0.9166667</td>\n",
       "      <td>0.89117646</td>\n",
       "      <td>0.91033137</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.79761904</td>\n",
       "      <td>0.91875</td>\n",
       "      <td>0.90909094</td>\n",
       "      <td>0.9344086</td>\n",
       "      <td>0.89181286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>mean_per_class_error</td>\n",
       "      <td>0.09229471</td>\n",
       "      <td>0.045092028</td>\n",
       "      <td>0.083333336</td>\n",
       "      <td>0.10882353</td>\n",
       "      <td>0.08966862</td>\n",
       "      <td>0.030303031</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.20238096</td>\n",
       "      <td>0.08125</td>\n",
       "      <td>0.09090909</td>\n",
       "      <td>0.065591395</td>\n",
       "      <td>0.10818713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>mse</td>\n",
       "      <td>0.08648799</td>\n",
       "      <td>0.028587071</td>\n",
       "      <td>0.09398208</td>\n",
       "      <td>0.08812845</td>\n",
       "      <td>0.0819581</td>\n",
       "      <td>0.046587806</td>\n",
       "      <td>0.048850812</td>\n",
       "      <td>0.13928667</td>\n",
       "      <td>0.08020601</td>\n",
       "      <td>0.12132377</td>\n",
       "      <td>0.07250852</td>\n",
       "      <td>0.09204771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>pr_auc</td>\n",
       "      <td>0.9728198</td>\n",
       "      <td>0.017460583</td>\n",
       "      <td>0.97514373</td>\n",
       "      <td>0.9732472</td>\n",
       "      <td>0.97362715</td>\n",
       "      <td>0.9925447</td>\n",
       "      <td>0.9967534</td>\n",
       "      <td>0.9425119</td>\n",
       "      <td>0.97766507</td>\n",
       "      <td>0.9481482</td>\n",
       "      <td>0.98478395</td>\n",
       "      <td>0.96377254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.946099</td>\n",
       "      <td>0.05632868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9310345</td>\n",
       "      <td>0.9259259</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.96428573</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.96666664</td>\n",
       "      <td>0.9230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>r2</td>\n",
       "      <td>0.6293815</td>\n",
       "      <td>0.109396346</td>\n",
       "      <td>0.5929286</td>\n",
       "      <td>0.61828285</td>\n",
       "      <td>0.6619428</td>\n",
       "      <td>0.77021027</td>\n",
       "      <td>0.7846493</td>\n",
       "      <td>0.41521707</td>\n",
       "      <td>0.6464252</td>\n",
       "      <td>0.5137858</td>\n",
       "      <td>0.6700472</td>\n",
       "      <td>0.6203256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.9069779</td>\n",
       "      <td>0.052783277</td>\n",
       "      <td>0.8333333</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9259259</td>\n",
       "      <td>0.93939394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9285714</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8181818</td>\n",
       "      <td>0.9354839</td>\n",
       "      <td>0.8888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>rmse</td>\n",
       "      <td>0.29039782</td>\n",
       "      <td>0.048956916</td>\n",
       "      <td>0.30656496</td>\n",
       "      <td>0.29686436</td>\n",
       "      <td>0.28628325</td>\n",
       "      <td>0.21584208</td>\n",
       "      <td>0.2210222</td>\n",
       "      <td>0.3732113</td>\n",
       "      <td>0.28320664</td>\n",
       "      <td>0.34831563</td>\n",
       "      <td>0.26927406</td>\n",
       "      <td>0.30339366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    mean           sd   cv_1_valid  \\\n",
       "0                  accuracy    0.9091582  0.037846405   0.89361703   \n",
       "1                       auc    0.9490777  0.026368229         0.95   \n",
       "2                     aucpr    0.9728198  0.017460583   0.97514373   \n",
       "3                       err  0.090841815  0.037846405   0.10638298   \n",
       "4                 err_count          4.2    1.7511901          5.0   \n",
       "5                  f0point5     0.936653  0.042016745   0.96153843   \n",
       "6                        f1   0.92409444  0.031948812   0.90909094   \n",
       "7                        f2   0.91328746   0.04084424   0.86206895   \n",
       "8            lift_top_group    1.6218984   0.19049643    1.5666667   \n",
       "9                   logloss   0.27318785   0.07786334    0.2911743   \n",
       "10      max_per_class_error   0.13681123  0.078728095   0.16666667   \n",
       "11                      mcc   0.81240034   0.07810417   0.80245835   \n",
       "12  mean_per_class_accuracy    0.9077053  0.045092028    0.9166667   \n",
       "13     mean_per_class_error   0.09229471  0.045092028  0.083333336   \n",
       "14                      mse   0.08648799  0.028587071   0.09398208   \n",
       "15                   pr_auc    0.9728198  0.017460583   0.97514373   \n",
       "16                precision     0.946099   0.05632868          1.0   \n",
       "17                       r2    0.6293815  0.109396346    0.5929286   \n",
       "18                   recall    0.9069779  0.052783277    0.8333333   \n",
       "19                     rmse   0.29039782  0.048956916   0.30656496   \n",
       "\n",
       "    cv_2_valid  cv_3_valid   cv_4_valid   cv_5_valid  cv_6_valid  cv_7_valid  \\\n",
       "0   0.89361703   0.9130435   0.95652175   0.95652175  0.82608694   0.9130435   \n",
       "1    0.9411765  0.94736844   0.97902095      0.99375   0.8968254   0.9479167   \n",
       "2    0.9732472  0.97362715    0.9925447    0.9967534   0.9425119  0.97766507   \n",
       "3   0.10638298  0.08695652   0.04347826   0.04347826  0.17391305  0.08695652   \n",
       "4          5.0         4.0          2.0          2.0         8.0         4.0   \n",
       "5    0.9246575   0.9259259    0.9872612    0.9493671   0.8333333   0.9507042   \n",
       "6   0.91525424   0.9259259      0.96875    0.9677419   0.8666667   0.9310345   \n",
       "7   0.90604025   0.9259259    0.9509202    0.9868421   0.9027778   0.9121622   \n",
       "8    1.5666667   1.7037038    1.3939394    1.5333333   1.6428572   1.5333333   \n",
       "9    0.2735675   0.2850713   0.15630236   0.16591774  0.41360745   0.2591859   \n",
       "10  0.11764706  0.10526316  0.060606062        0.125  0.33333334         0.1   \n",
       "11   0.7733081   0.8206628   0.90229785   0.90571105  0.63134533   0.8173163   \n",
       "12  0.89117646  0.91033137     0.969697       0.9375  0.79761904     0.91875   \n",
       "13  0.10882353  0.08966862  0.030303031       0.0625  0.20238096     0.08125   \n",
       "14  0.08812845   0.0819581  0.046587806  0.048850812  0.13928667  0.08020601   \n",
       "15   0.9732472  0.97362715    0.9925447    0.9967534   0.9425119  0.97766507   \n",
       "16   0.9310345   0.9259259          1.0       0.9375      0.8125  0.96428573   \n",
       "17  0.61828285   0.6619428   0.77021027    0.7846493  0.41521707   0.6464252   \n",
       "18         0.9   0.9259259   0.93939394          1.0   0.9285714         0.9   \n",
       "19  0.29686436  0.28628325   0.21584208    0.2210222   0.3732113  0.28320664   \n",
       "\n",
       "    cv_8_valid   cv_9_valid cv_10_valid  \n",
       "0    0.9130435    0.9347826   0.8913044  \n",
       "1   0.93560606   0.96344084   0.9356725  \n",
       "2    0.9481482   0.98478395  0.96377254  \n",
       "3   0.08695652   0.06521739  0.10869565  \n",
       "4          4.0          3.0         5.0  \n",
       "5    0.9574468    0.9602649   0.9160305  \n",
       "6          0.9    0.9508197   0.9056604  \n",
       "7    0.8490566    0.9415584   0.8955224  \n",
       "8     2.090909     1.483871   1.7037038  \n",
       "9   0.35825554    0.2358886  0.29290783  \n",
       "10  0.18181819   0.06666667  0.11111111  \n",
       "11   0.8374358    0.8551342   0.7783335  \n",
       "12  0.90909094    0.9344086  0.89181286  \n",
       "13  0.09090909  0.065591395  0.10818713  \n",
       "14  0.12132377   0.07250852  0.09204771  \n",
       "15   0.9481482   0.98478395  0.96377254  \n",
       "16         1.0   0.96666664   0.9230769  \n",
       "17   0.5137858    0.6700472   0.6203256  \n",
       "18   0.8181818    0.9354839   0.8888889  \n",
       "19  0.34831563   0.26927406  0.30339366  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>duration</th>\n",
       "      <th>number_of_trees</th>\n",
       "      <th>training_rmse</th>\n",
       "      <th>training_logloss</th>\n",
       "      <th>training_auc</th>\n",
       "      <th>training_pr_auc</th>\n",
       "      <th>training_lift</th>\n",
       "      <th>training_classification_error</th>\n",
       "      <th>validation_rmse</th>\n",
       "      <th>validation_logloss</th>\n",
       "      <th>validation_auc</th>\n",
       "      <th>validation_pr_auc</th>\n",
       "      <th>validation_lift</th>\n",
       "      <th>validation_classification_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.234 sec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.515204</td>\n",
       "      <td>0.724994</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500867</td>\n",
       "      <td>0.469900</td>\n",
       "      <td>0.633854</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.237 sec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.411792</td>\n",
       "      <td>0.514231</td>\n",
       "      <td>0.919268</td>\n",
       "      <td>0.942009</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.119584</td>\n",
       "      <td>0.377229</td>\n",
       "      <td>0.454610</td>\n",
       "      <td>0.926471</td>\n",
       "      <td>0.969823</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.241 sec</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.355954</td>\n",
       "      <td>0.410571</td>\n",
       "      <td>0.934478</td>\n",
       "      <td>0.953985</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.097054</td>\n",
       "      <td>0.341621</td>\n",
       "      <td>0.380535</td>\n",
       "      <td>0.915441</td>\n",
       "      <td>0.964827</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.244 sec</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.324600</td>\n",
       "      <td>0.352842</td>\n",
       "      <td>0.946217</td>\n",
       "      <td>0.961230</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.091854</td>\n",
       "      <td>0.320610</td>\n",
       "      <td>0.336196</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.973033</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.248 sec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.302636</td>\n",
       "      <td>0.314255</td>\n",
       "      <td>0.958309</td>\n",
       "      <td>0.969227</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.091854</td>\n",
       "      <td>0.309987</td>\n",
       "      <td>0.313118</td>\n",
       "      <td>0.948529</td>\n",
       "      <td>0.977099</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.251 sec</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.286289</td>\n",
       "      <td>0.285813</td>\n",
       "      <td>0.960562</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.084922</td>\n",
       "      <td>0.303158</td>\n",
       "      <td>0.294418</td>\n",
       "      <td>0.948529</td>\n",
       "      <td>0.977099</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.255 sec</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.272416</td>\n",
       "      <td>0.262899</td>\n",
       "      <td>0.968654</td>\n",
       "      <td>0.976196</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.077990</td>\n",
       "      <td>0.297965</td>\n",
       "      <td>0.281730</td>\n",
       "      <td>0.948529</td>\n",
       "      <td>0.977099</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.259 sec</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.264653</td>\n",
       "      <td>0.249598</td>\n",
       "      <td>0.971015</td>\n",
       "      <td>0.977754</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.077990</td>\n",
       "      <td>0.290045</td>\n",
       "      <td>0.267339</td>\n",
       "      <td>0.948529</td>\n",
       "      <td>0.977099</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.263 sec</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.258142</td>\n",
       "      <td>0.238939</td>\n",
       "      <td>0.974012</td>\n",
       "      <td>0.980063</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.067591</td>\n",
       "      <td>0.283816</td>\n",
       "      <td>0.256250</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.979527</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.267 sec</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.247310</td>\n",
       "      <td>0.223192</td>\n",
       "      <td>0.978596</td>\n",
       "      <td>0.983378</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.060659</td>\n",
       "      <td>0.283850</td>\n",
       "      <td>0.253987</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.979527</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.271 sec</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.240937</td>\n",
       "      <td>0.213795</td>\n",
       "      <td>0.980777</td>\n",
       "      <td>0.984846</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.058925</td>\n",
       "      <td>0.280878</td>\n",
       "      <td>0.249270</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.979527</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.275 sec</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.235231</td>\n",
       "      <td>0.206158</td>\n",
       "      <td>0.982038</td>\n",
       "      <td>0.985917</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.057192</td>\n",
       "      <td>0.278207</td>\n",
       "      <td>0.245439</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.986277</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>2020-05-18 15:28:05</td>\n",
       "      <td>3.278 sec</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.232593</td>\n",
       "      <td>0.202440</td>\n",
       "      <td>0.982837</td>\n",
       "      <td>0.986451</td>\n",
       "      <td>2.003472</td>\n",
       "      <td>0.060659</td>\n",
       "      <td>0.276337</td>\n",
       "      <td>0.242529</td>\n",
       "      <td>0.963235</td>\n",
       "      <td>0.983586</td>\n",
       "      <td>1.470588</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    duration  number_of_trees  training_rmse  \\\n",
       "0     2020-05-18 15:28:05   3.234 sec              0.0       0.515204   \n",
       "1     2020-05-18 15:28:05   3.237 sec              5.0       0.411792   \n",
       "2     2020-05-18 15:28:05   3.241 sec             10.0       0.355954   \n",
       "3     2020-05-18 15:28:05   3.244 sec             15.0       0.324600   \n",
       "4     2020-05-18 15:28:05   3.248 sec             20.0       0.302636   \n",
       "5     2020-05-18 15:28:05   3.251 sec             25.0       0.286289   \n",
       "6     2020-05-18 15:28:05   3.255 sec             30.0       0.272416   \n",
       "7     2020-05-18 15:28:05   3.259 sec             35.0       0.264653   \n",
       "8     2020-05-18 15:28:05   3.263 sec             40.0       0.258142   \n",
       "9     2020-05-18 15:28:05   3.267 sec             45.0       0.247310   \n",
       "10    2020-05-18 15:28:05   3.271 sec             50.0       0.240937   \n",
       "11    2020-05-18 15:28:05   3.275 sec             55.0       0.235231   \n",
       "12    2020-05-18 15:28:05   3.278 sec             57.0       0.232593   \n",
       "\n",
       "    training_logloss  training_auc  training_pr_auc  training_lift  \\\n",
       "0           0.724994      0.500000         0.499133       1.000000   \n",
       "1           0.514231      0.919268         0.942009       2.003472   \n",
       "2           0.410571      0.934478         0.953985       2.003472   \n",
       "3           0.352842      0.946217         0.961230       2.003472   \n",
       "4           0.314255      0.958309         0.969227       2.003472   \n",
       "5           0.285813      0.960562         0.970730       2.003472   \n",
       "6           0.262899      0.968654         0.976196       2.003472   \n",
       "7           0.249598      0.971015         0.977754       2.003472   \n",
       "8           0.238939      0.974012         0.980063       2.003472   \n",
       "9           0.223192      0.978596         0.983378       2.003472   \n",
       "10          0.213795      0.980777         0.984846       2.003472   \n",
       "11          0.206158      0.982038         0.985917       2.003472   \n",
       "12          0.202440      0.982837         0.986451       2.003472   \n",
       "\n",
       "    training_classification_error  validation_rmse  validation_logloss  \\\n",
       "0                        0.500867         0.469900            0.633854   \n",
       "1                        0.119584         0.377229            0.454610   \n",
       "2                        0.097054         0.341621            0.380535   \n",
       "3                        0.091854         0.320610            0.336196   \n",
       "4                        0.091854         0.309987            0.313118   \n",
       "5                        0.084922         0.303158            0.294418   \n",
       "6                        0.077990         0.297965            0.281730   \n",
       "7                        0.077990         0.290045            0.267339   \n",
       "8                        0.067591         0.283816            0.256250   \n",
       "9                        0.060659         0.283850            0.253987   \n",
       "10                       0.058925         0.280878            0.249270   \n",
       "11                       0.057192         0.278207            0.245439   \n",
       "12                       0.060659         0.276337            0.242529   \n",
       "\n",
       "    validation_auc  validation_pr_auc  validation_lift  \\\n",
       "0         0.500000           0.680000         1.000000   \n",
       "1         0.926471           0.969823         1.470588   \n",
       "2         0.915441           0.964827         1.470588   \n",
       "3         0.941176           0.973033         1.470588   \n",
       "4         0.948529           0.977099         1.470588   \n",
       "5         0.948529           0.977099         1.470588   \n",
       "6         0.948529           0.977099         1.470588   \n",
       "7         0.948529           0.977099         1.470588   \n",
       "8         0.955882           0.979527         1.470588   \n",
       "9         0.955882           0.979527         1.470588   \n",
       "10        0.955882           0.979527         1.470588   \n",
       "11        0.970588           0.986277         1.470588   \n",
       "12        0.963235           0.983586         1.470588   \n",
       "\n",
       "    validation_classification_error  \n",
       "0                              0.32  \n",
       "1                              0.16  \n",
       "2                              0.16  \n",
       "3                              0.12  \n",
       "4                              0.08  \n",
       "5                              0.08  \n",
       "6                              0.08  \n",
       "7                              0.08  \n",
       "8                              0.08  \n",
       "9                              0.08  \n",
       "10                             0.08  \n",
       "11                             0.08  \n",
       "12                             0.08  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>relative_importance</th>\n",
       "      <th>scaled_importance</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Internal_Audit_Score</td>\n",
       "      <td>365.931274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.635384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Fin_Score</td>\n",
       "      <td>70.837288</td>\n",
       "      <td>0.193581</td>\n",
       "      <td>0.122998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>External_Audit_Score</td>\n",
       "      <td>46.943100</td>\n",
       "      <td>0.128284</td>\n",
       "      <td>0.081510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>City</td>\n",
       "      <td>42.315247</td>\n",
       "      <td>0.115637</td>\n",
       "      <td>0.073474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Location_Score</td>\n",
       "      <td>39.970921</td>\n",
       "      <td>0.109231</td>\n",
       "      <td>0.069403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Loss_score</td>\n",
       "      <td>9.906520</td>\n",
       "      <td>0.027072</td>\n",
       "      <td>0.017201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Past_Results</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               variable  relative_importance  scaled_importance  percentage\n",
       "0  Internal_Audit_Score           365.931274           1.000000    0.635384\n",
       "1             Fin_Score            70.837288           0.193581    0.122998\n",
       "2  External_Audit_Score            46.943100           0.128284    0.081510\n",
       "3                  City            42.315247           0.115637    0.073474\n",
       "4        Location_Score            39.970921           0.109231    0.069403\n",
       "5            Loss_score             9.906520           0.027072    0.017201\n",
       "6          Past_Results             0.016807           0.000046    0.000029"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<bound method H2OBinomialModel.confusion_matrix of >"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leader.confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">       p0</th><th style=\"text-align: right;\">       p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.013286 </td><td style=\"text-align: right;\">0.986714 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.0561939</td><td style=\"text-align: right;\">0.943806 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.907593 </td><td style=\"text-align: right;\">0.0924075</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.010616 </td><td style=\"text-align: right;\">0.989384 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.213246 </td><td style=\"text-align: right;\">0.786754 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.783477 </td><td style=\"text-align: right;\">0.216523 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.920086 </td><td style=\"text-align: right;\">0.0799136</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.893444 </td><td style=\"text-align: right;\">0.106556 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.152804 </td><td style=\"text-align: right;\">0.847196 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.0111838</td><td style=\"text-align: right;\">0.988816 </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export File progress: |███████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "h2o.export_file(pred_df, path ='C:\\\\Users\\\\niran\\\\Documents\\\\Python DSE-jan\\\\Machinehack\\\\3.Financial risk prediction\\\\Financial_Risk_Participants_Data\\\\Financial_Risk_Participants_Data\\\\auto_gbm_0.85_0.1_10fold_balancetrue_seed33_cap', force = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The above prediction got me into top 3 in final leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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